The present disclosure is related to a memory device comprising a plurality of resistive elements.
The present disclosure further concerns a related method and a related computer program product.
Nanoscale memory devices, whose resistance depends on the history of the electric signals applied, could become building blocks in new computing paradigms, such as brain-inspired computing and memcomputing.
One promising example for resistive memory devices is phase-change memory (PCM) devices. PCM is a non-volatile solid-state memory technology that exploits the reversible, thermally-assisted switching of phase-change materials, in particular chalcogenide compounds such as GST (Germanium-Antimony-Tellurium), between states with different electrical resistance. The fundamental storage unit (the “cell”) can be programmed into a number of different states, or levels, which exhibit different resistance characteristics. The programmable cell-states can be used to represent different data values, permitting storage of information.
Reading and writing of data in PCM cells is achieved by applying appropriate voltages to the phase-change material via a pair of electrodes associated with each cell. In a write operation, the resulting programming signal causes Joule heating of the phase-change material to an appropriate temperature to induce the desired cell-state on cooling. Reading of PCM cells is performed using cell resistance as a metric for cell-state. An applied read voltage causes current to flow through the cell, this current being dependent on resistance of the cell. Measurement of the cell current therefore provides an indication of the programmed cell state. A sufficiently low read voltage is used for this resistance metric to ensure that application of the read voltage does not disturb the programmed cell state. Cell state detection can then be performed by comparing the resistance metric with predefined reference levels for the programmable cell-states.
Cognitive computing is a promising technology for deriving intelligence and knowledge from huge volumes of data. Today's cognitive computers are usually based on the Von Neumann architecture in which the computing and the memory units are separated. Cognitive computing is inherently data-centric, meaning that huge amounts of data need to be shuttled back and forth at high speeds. As the Von Neumann architecture is rather inefficient for such a task, it is becoming increasingly clear that other architectures are desired to build efficient cognitive computers, in particular architectures where memory and logic coexist in some form.
Memcomputing is a non-Von Neumann approach being researched. An element in this computing paradigm is a high-density, low-power, variable state, programmable and non-volatile memory device.
A fundamental computational primitive is a matrix-vector multiplication. This primitive is of particular interest as it forms the basis of several linear algebraic operations and it is one of the most commonly used mathematical operations in science and engineering. A matrix is usually represented by a two-dimensional array of matrix elements and a vector by a one-dimensional array of vector elements. A matrix may be considered as array of vectors. Hence a matrix-vector multiplication can be generalized to a matrix-matrix multiplication and to a vector-vector multiplication.
However, there are key challenges to overcome, such as the high programming power required, noise and resistance drift.
Accordingly there is a need for further improvements of memory devices comprising resistive elements.